Surface ozone (O₃) is considered an hazard to human health, affecting vegetation crops and ecosystems. Accurate time and location O₃ forecasting can help to protect citizens to unhealthy exposures when high levels are expected. Usually, forecasting models use numerous O₃ precursors as predictors, limiting the reproducibility of these models to the availability of such information from data providers. This study introduces a 24 h-ahead hourly O₃ concentrations forecasting methodology based on bagging and ensemble learning, using just two predictors with lagged O₃ concentrations. This methodology was applied on ten-year time series (2006–2015) from three major urban areas of Andalusia (Spain). Its forecasting performance was contrasted with an algorithm especially designed to forecast time series exhibiting temporal patterns. The proposed methodology outperforms the contrast algorithm and yields comparable results to others existing in literature. Its use is encouraged due to its forecasting performance and wide applicability, but also as benchmark methodology.

表面臭氧（O ₃）被认为是对人类健康的危害，影响到植物和生态系统。准确的时间和位置O ₃预测可以在预期到高水平的情况下帮助保护市民避免不健康的接触。通常，预测模型使用大量的O ₃前兆作为预测因子，从而将这些模型的可重复性限制为可从数据提供者获得此类信息。这项研究引入了基于套袋和整体学习的提前24小时每小时O ₃浓度预测方法，仅使用了两个O ₃滞后的预测因子浓度。该方法适用于安达卢西亚（西班牙）三个主要城市地区的十年时间序列（2006-2015年）。它的预测性能与专门设计用来预测显示时间模式的时间序列的算法形成对比。所提出的方法优于对比度算法，并且产生的结果可与文献中存在的其他方法进行比较。由于其预测性能和广泛适用性，因此鼓励使用该方法，但也将其作为基准方法。